Change the name "LossRecord" to "MetricsTracker"

egs/librispeech/ASR/conformer_ctc/train.py

@@ -46,7 +46,7 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
-    LossRecord,
+    MetricsTracker,
     encode_supervisions,
     setup_logger,
     str2bool,
@@ -291,7 +291,7 @@ def compute_loss(
     batch: dict,
     graph_compiler: BpeCtcTrainingGraphCompiler,
     is_training: bool,
-) -> Tuple[Tensor, LossRecord]:
+) -> Tuple[Tensor, MetricsTracker]:
     """
     Compute CTC loss given the model and its inputs.
 
@@ -373,7 +373,7 @@ def compute_loss(
 
     assert loss.requires_grad == is_training
 
-    info = LossRecord()
+    info = MetricsTracker()
     info["frames"] = supervision_segments[:, 2].sum().item()
     info["ctc_loss"] = ctc_loss.detach().cpu().item()
     if params.att_rate != 0.0:
@@ -390,11 +390,11 @@ def compute_validation_loss(
     graph_compiler: BpeCtcTrainingGraphCompiler,
     valid_dl: torch.utils.data.DataLoader,
     world_size: int = 1,
-) -> LossRecord:
+) -> MetricsTracker:
     """Run the validation process."""
     model.eval()
 
-    tot_loss = LossRecord()
+    tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(valid_dl):
         loss, loss_info = compute_loss(
@@ -454,7 +454,7 @@ def train_one_epoch(
     """
     model.train()
 
-    tot_loss = LossRecord()
+    tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(train_dl):
         params.batch_idx_train += 1

egs/librispeech/ASR/tdnn_lstm_ctc/train.py

@@ -45,7 +45,7 @@ from icefall.graph_compiler import CtcTrainingGraphCompiler
 from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
-    LossRecord,
+    MetricsTracker,
     encode_supervisions,
     setup_logger,
     str2bool,
@@ -270,7 +270,7 @@ def compute_loss(
     batch: dict,
     graph_compiler: CtcTrainingGraphCompiler,
     is_training: bool,
-) -> Tuple[Tensor, LossRecord]:
+) -> Tuple[Tensor, MetricsTracker]:
     """
     Compute CTC loss given the model and its inputs.
 
@@ -327,7 +327,7 @@ def compute_loss(
 
     assert loss.requires_grad == is_training
 
-    info = LossRecord()
+    info = MetricsTracker()
     info["frames"] = supervision_segments[:, 2].sum().item()
     info["loss"] = loss.detach().cpu().item()
 
@@ -340,13 +340,13 @@ def compute_validation_loss(
     graph_compiler: CtcTrainingGraphCompiler,
     valid_dl: torch.utils.data.DataLoader,
     world_size: int = 1,
-) -> LossRecord:
+) -> MetricsTracker:
     """Run the validation process. The validation loss
     is saved in `params.valid_loss`.
     """
     model.eval()
 
-    tot_loss = LossRecord()
+    tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(valid_dl):
         loss, loss_info = compute_loss(
@@ -408,7 +408,7 @@ def train_one_epoch(
     """
     model.train()
 
-    tot_loss = LossRecord()
+    tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(train_dl):
         params.batch_idx_train += 1

egs/yesno/ASR/tdnn/train.py

@@ -24,7 +24,7 @@ from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.graph_compiler import CtcTrainingGraphCompiler
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, LossRecord, setup_logger, str2bool
+from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
 
 def get_parser():
@@ -248,7 +248,7 @@ def compute_loss(
     batch: dict,
     graph_compiler: CtcTrainingGraphCompiler,
     is_training: bool,
-) -> Tuple[Tensor, LossRecord]:
+) -> Tuple[Tensor, MetricsTracker]:
     """
     Compute CTC loss given the model and its inputs.
 
@@ -308,7 +308,7 @@ def compute_loss(
 
     assert loss.requires_grad == is_training
 
-    info = LossRecord()
+    info = MetricsTracker()
     info["frames"] = supervision_segments[:, 2].sum().item()
     info["loss"] = loss.detach().cpu().item()
 
@@ -321,13 +321,13 @@ def compute_validation_loss(
     graph_compiler: CtcTrainingGraphCompiler,
     valid_dl: torch.utils.data.DataLoader,
     world_size: int = 1,
-) -> LossRecord:
+) -> MetricsTracker:
     """Run the validation process. The validation loss
     is saved in `params.valid_loss`.
     """
     model.eval()
 
-    tot_loss = LossRecord()
+    tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(valid_dl):
         loss, loss_info = compute_loss(
@@ -389,7 +389,7 @@ def train_one_epoch(
     """
     model.train()
 
-    tot_loss = LossRecord()
+    tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(train_dl):
         params.batch_idx_train += 1

icefall/utils.py

@@ -424,22 +424,24 @@ def write_error_stats(
     return float(tot_err_rate)
 
 
-class LossRecord(collections.defaultdict):
+class MetricsTracker(collections.defaultdict):
     def __init__(self):
         # Passing the type 'int' to the base-class constructor
         # makes undefined items default to int() which is zero.
-        super(LossRecord, self).__init__(int)
+        # This class will play a role as metrics tracker.
+        # It can record many metrics, including but not limited to loss.
+        super(MetricsTracker, self).__init__(int)
 
-    def __add__(self, other: "LossRecord") -> "LossRecord":
+    def __add__(self, other: "MetricsTracker") -> "MetricsTracker":
-        ans = LossRecord()
+        ans = MetricsTracker()
         for k, v in self.items():
             ans[k] = v
         for k, v in other.items():
             ans[k] = ans[k] + v
         return ans
 
-    def __mul__(self, alpha: float) -> "LossRecord":
+    def __mul__(self, alpha: float) -> "MetricsTracker":
-        ans = LossRecord()
+        ans = MetricsTracker()
         for k, v in self.items():
             ans[k] = v * alpha
         return ans